Abstract
For robots with joint elasticity, discrepancies exist between the motor side and the load side. Thus the load side (end-effector) performance can hardly be guaranteed with motor side measurements alone. In this paper, a computationally easy load side state estimation scheme is proposed for the robots with joint elasticity, which is equipped with motor encoders and a low-cost end-effector MEMS sensor such as 3-axial accelerometer. An optimization based inverse differential kinematics algorithm is developed to obtain the load side joint acceleration estimate. Then the joint position and velocity estimation problem is decoupled into simple 2-order kinematic Kalman filters for each joint. Maximum likelihood principle is utilized to estimate the fictitious noise covariances. Both offline and online solutions are derived. The extension to other sensor configurations is discussed as well. The effectiveness of the developed method is validated through simulation and experimental study on a 6-DOF industrial robot.
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